Automatic detection of emergency Maneuvers, crashes, and strong jolts in naturalistic riding data from e-bicycles and e-scooters

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This study develops and validates a smartphone-based framework for automatically detecting emergency maneuvers, strong jolts, and crashes involving electric scooters and electric bicycles. Detection criteria were established through controlled track experiments and subsequently evaluated using data collected during a naturalistic riding study involving 119 participants and more than 26,000 km and 1,600 h of riding, combining accelerometer, gyroscope, GPS, and video recordings. Threshold-based detection criteria were defined using variables selected for their physical relevance and ability to discriminate between target and non-target situations. Hard braking, sharp turns, strong jolts, and crash-related events were identified using combinations of acceleration, jerk, rotational dynamics, and post-event vehicle motion. Video review showed that 74% of hard-braking detections corresponded to harsh-braking maneuvers, 64% of sharp-turn detections reflected genuine avoidance maneuvers, and 91% of strong-jolt detections were associated with infrastructure features. Video verification of collision candidates confirmed several reported and previously unreported impacts, including collisions with other road users and single-vehicle falls. Application of the framework to the naturalistic dataset revealed marked differences between vehicle types. E-scooter users experienced higher rates of hard braking and strong jolts than e-bicycle users, reflecting behavioral differences and vehicle characteristics. Illustrative mapping examples showed that detected events and rider-reported hazardous situations could occur in close proximity, suggesting opportunities for future spatial analyses of micromobility safety. Although additional validation on larger crash datasets is required, the results demonstrate that threshold-based approaches can provide meaningful indicators of rider safety, support large-scale monitoring of micromobility risks, and contribute to infrastructure and transport-safety assessment.

Links and sources

• PDF from original source

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